Dynamic vehicle routing for regional clusters

ABSTRACT

Techniques described herein are directed towards dynamically adding new pickup orders to an existing route structure. In at least some embodiments, a service provider separates a number of vendors into separate clusters based on one or more attributes. For example, the vendors may be separated by geographic region. The service provider may then identify a set of routes that are associated with each of the vendor clusters and run optimization techniques on that set of routes. The set of routes may be filtered based on attributes of the route. In some embodiments, the service provider may make a route alteration that accommodates the new pickup order.

BACKGROUND

Fulfillment centers may receive inventory from a number of differentvendors. When the fulfillment center is responsible for collecting thisinventory, efficient route management can become nearly impossible. Thisis especially true when some of the vendors are individuals or one-timesellers, as is frequently the case with online sales fulfillment. As thenumber of online merchants continues to increase, these challenges areonly amplified.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIG. 1 depicts an illustrative example pickup assignment method inaccordance with at least some embodiments of the disclosure;

FIG. 2 depicts an illustrative vendor clustering technique that may beperformed by a clustering module in accordance with at least someembodiments;

FIG. 3 depicts an illustrative example of dynamic vehicle routing thatmay be implemented for a regional cluster in accordance with at leastone embodiment;

FIG. 4 depicts an illustrative example of a system or architecture inwhich techniques for dynamically assigning a new order to pickup routesmay be implemented in accordance with at least some embodiments;

FIG. 5 depicts an illustrative example of an inbound vehicle routingplatform that dynamically adds a new order to pickup routes;

FIG. 6 depicts an illustrative flow chart demonstrating an example routealteration technique in accordance with at least some embodiments;

FIG. 7 depicts an illustrative flow diagram depicting an example routeupdating process in accordance with at least some embodiments; and

FIG. 8 depicts an illustrative environment in which various embodimentscan be implemented.

DETAILED DESCRIPTION

In the following description, various embodiments will be described. Forpurposes of explanation, specific configurations and details are setforth in order to provide a thorough understanding of the embodiments.However, it will also be apparent to one skilled in the art that theembodiments may be practiced without the specific details. Furthermore,well-known features may be omitted or simplified in order not to obscurethe embodiment being described.

Techniques described herein are directed to dynamically adding inboundshipments from vendors to existing routes. In particular, the disclosureis directed to assigning inbound pickups to an appropriate route as theinbound pickups are scheduled. In some embodiments, one or more vendorsmay be grouped or clustered by region and a processor device mayseparately process the routes associated with that region. In someembodiments, routes may be planned on a per region basis.

In an illustrative example, a service provider may receive inboundshipments from a multitude of vendors located throughout the world. Inthis example, the service provider may be responsible for physicallypicking up the inbound shipments from each vendor. To do thisefficiently, the vendors may be grouped locally, such as by region. Eachregion may then be associated with a set of pickup routes (also calledlanes). When one of the vendors indicates a shipment is ready to bepicked up, the service provider may perform a number of optimizationtechniques on each of the pickup routes associated with the vendor'sregion in order to determine which pickup route should be altered toinclude a pickup of the shipment. By separating the vendors by region,the service provider makes the optimization more computationallyfeasible. Additionally, this allows dynamic updating of route andshipment information.

In accordance with at least some embodiments, vendors may be clusteredby geographic region. For example, the service provider may identifyshipment origination regions that have a high concentration of vendors.In this example, the service provider may put each of the shipmentregions into a separate cluster. In some embodiments, the serviceprovider may associate particular routes with a region cluster. Forexample, a pickup route may be associated with the region cluster inwhich it originates, passes through, or has a pickup. When a particularvendor indicates that a shipment is ready to be picked up, the serviceprovider may identify the region associated with the vendor, identifythe routes associated with that region, and select the route that ismost appropriate for fulfilling the shipment pickup. In at least someembodiments, a new vendor may be associated with a region cluster whenit indicates that a shipment is ready to be fulfilled. For example, athird party entity may indicate that he or she has an item to sell viaan electronic marketplace and request pickup. In this example, theservice provider of the electronic marketplace may provide shipment andfulfillment services. The service provider may determine the regioncluster associated with the third party entity based on the third partyentity's distance to a cluster's center (centroid). The service providermay also select a pickup route, from those that service the regioncluster, to fulfill the request.

In accordance with at least some embodiments, the service provider mayrun one or more optimization techniques on the associated pickup routesin order to identify an optimal route for fulfilling an order pickup. Insome embodiments, the service provider may alter more than one route.For example, in at least some embodiments, the service provider mayrecalculate each of the pickup routes associated with the regioncluster. In at least some embodiments, each region may be processed by aseparate server or processing device. In these embodiments, multipleroutes may be calculated in parallel.

Although this disclosure focuses on pickup routes including inboundshipment orders, it should be recognized that this is only forillustrative purposes. One skilled in the art would recognize that thedescribed regional clustering processes and route planning processes maybe applied to other transportation/logistics processes. For example, thetechniques described in this disclosure may equally apply to orderdelivery, pickup of customer returns, transportation of goods betweenfulfillment centers, and/or any other suitable vehicle routing process.

FIG. 1 depicts an illustrative example pickup assignment method 100 inaccordance with at least some embodiments of the disclosure. In FIG. 1,several delivery vehicles 102, 104, and 106 are depicted on routes to afulfillment center 108. The delivery vehicles may be dispatched fromfulfillment center 108 or a number of other fulfillment centers 110 and112.

In accordance with at least some embodiments, a request related to ordershipment 114 may be received by a service provider. In this example, theservice provider may identify a region cluster 113 in which the shipmentpickup is to be made. The service provider may then determine thatdelivery vehicles 102 and 104 pass through the region cluster 113. Theservice provider may then determine that, between the available deliveryvehicles, it is more desirable for delivery vehicle 104 to pick up ordershipment 114. The determination may be based at least in part on cost,pickup times, delivery estimates, service level agreements (SLAs),combinations of the foregoing or the like. Upon making thisdetermination, the service provider may alter delivery vehicle's currentpickup route 116 to a new route 118 in order to fulfill pickup of theorder shipment 114.

By way of further illustration, the service provider may receive asecond request related to order shipment 120. In this example, theservice provider may identify that order shipment 120 is associated witha second region cluster 121. The service provider may then determinethat delivery vehicles 104 and 106 pass through this second regioncluster 121. Once again, the service provider may determine whichavailable vehicle should pick up the order (e.g., in order to minimizecosts, travel distance, travel time, etc.). In this example, the serviceprovider may compare the current pickup route 122 of the deliveryvehicle 106 to the new pickup route 118 of the delivery vehicle 104.Although pickup route 116 may have been the optimal choice for pickingup order shipment 120, the new pickup route 118 may be less thanoptimal. Upon making this determination, the service provider may alterdelivery vehicle's 106 current pickup route 122 to a new route 124 inorder to fulfill pickup of the order shipment 120.

In some embodiments, the service provider may elect to add a pickup to aparticular route based on the accessibility of the pickup location andthe type of vehicle assigned to the pickup route. For example, a smallpickup order may be serviced by a bicycle messenger that is scheduled topick up other orders in the area, but a large pickup order may require adelivery truck. By way of a second example, a pickup order may require aforklift to be loaded onto a delivery vehicle. In this example, a largervehicle may be required to collect the pickup order.

FIG. 2 depicts an illustrative vendor clustering technique that may beperformed by a clustering module in accordance with at least someembodiments. In FIG. 2, a geographic region 202 is depicted. Asillustrated, multiple individual pickup locations 204 may be dispersedthroughout geographic region 202. A pickup location 204 may be anyaddress or location that is associated with a vendor and at which apickup has been scheduled (either currently or in the past), as well asforecasted pickups in the future. A particular geographic region 202 maybe of any size. For example, the geographic region may include theentire United States.

In accordance with at least some embodiments, the clustering module mayidentify high density pickup areas 206 within the geographic region 202.For example, a high density pickup area 206 may consist of a warehouseor industrial district within a city or other urban area. In someembodiments, a high density pickup area 206 may include an entire cityor region. Once the service provider has identified a number of highdensity pickup areas 206, the service provider may break the geographicarea 202 into regional clusters 208. In some embodiments, boundary lines210 for regional clusters 208 may run along natural barriers (e.g., ariver or a mountain range). In some embodiments, regional clusters 208may be constrained by man-made boundaries. For example, a serviceprovider may separate the geographic area 202 into regional clusters 208that each include an area constrained by one or more postal codes (e.g.,three zip codes or the like). In some embodiments, a clustering moduleof the service provider may use a clustering algorithm (e.g., k-meansclustering) to assign regional clusters 208. For example, the serviceprovider may assign a number of centroids to the geographic area 202such that the distance between each of the pickup locations 204 and thecentroids are minimized. In this example, a regional cluster 208 isformed by assigning each pickup location 204 to the closest centroid.The points that are equally close to two or more centroids then form acentroid boundary 210. The service provider may adjust the number ofcentroids, which would result in a movement of cluster boundaries and anincrease or decrease in the number of regional clusters 208.

Once regional clusters have been created, they may be persisted for someduration. In other words, regional clusters may remain fixed for someperiod of time (one day, one month, one year, etc.) before beingrecalculated. In at least some embodiments, the duration may bedependent on a continuity of the volume of vendors, or how quickly thevendor population changes. For example, a service provider thatprimarily collects orders from the same set of vendors each time maypersist regional clusters for a longer duration than a service providerthat primarily collects orders from one-time vendors and/or individualsselling personal items. By way of further example, if a service providerselects new vendors and renews vendor contracts on a yearly basis, thenit may also make sense for that service provider to persist regionalclusters for the length of the contracts. In this example, the serviceprovider may use the clustering module to recalculate regional clustersevery year shortly after the new vendors are selected and any vendorcontracts are renewed. By persisting regional clusters in this fashion,the service provider may be able to plan routes and secure carriercapacity far in advance. Additionally, the service provider may utilizeregularly scheduled routes. For example, a particular set of vendors mayprovide goods every two weeks. In this example, the service provider mayhave a regularly scheduled pickup route to collect these goods. At thetime that a new pickup order is requested, the service provider may havea number of routes already in place within a particular regionalcluster. By adding the new pickup order to one of these already existingpickup routes, the service provider may be prevented from paying highercosts associated with securing last-minute resources. Additionally, itallows a service provider to better utilize delivery vehicle capacity,reducing overall shipping costs and transit times.

In at least some embodiments, the service provider may persist regionalclusters until it detects a change in at least one high-density pickuparea 206. For example, the service provider may detect that pickuporders from a high-density pickup area 206 have significantly decreased.In response, the service provider may use the clustering module torecalculate regional clusters. Alternatively, the service provider maydetect a large number of new pickup orders associated with a particularlocation. The service provider may then determine that this is a newhigh density pickup area 206 and may use the clustering module torecalculate regional clusters. In some embodiments, a number of clustersassociated with the clustering technique (e.g., k in a k-meansclustering algorithm) may be dependent on the number of high densitypickup areas detected by the service provider. In these embodiments, thenumber of clusters may be adjusted along with the number of high densitypickup areas.

In some embodiments, the size of each regional cluster may be adjustedto include more or fewer pickup locations 204. For example, the serviceprovider may set a maximum or minimum number of pickup locations thatare to be included in each regional cluster 208. In some embodiments, asthe number of total pickup locations changes, the number of centroidsmay be adjusted and regional cluster boundaries 210 reassigned.

FIG. 3 depicts an illustrative example of dynamic vehicle routing thatmay be implemented for a regional cluster in accordance with at leastone embodiment. In FIG. 3, a geographic area has been separated into anumber of regional clusters 302(1-M). In this example, M may be thetotal number of regional clusters created by a clustering module asdescribed in the previous figure. Each regional cluster 302(1-M) may beassigned a set of processor devices 304. For example, a single regionalcluster 302(1) may be assigned processor devices 304(1-N). In someembodiments, N is determined based on a number of pickup locations or anumber of routes currently assigned to the regional cluster 302(1).Although FIG. 3 depicts each of regional clusters 302(1-M) beingprocessed by one or more separate processors, some embodiments of thedisclosure may include one or more of regional clusters 302(1-M) beingprocessed on a shared processor or set of processor devices 304.

In some embodiments, a set of routes may be identified for a particularregional cluster. For example, each pickup that is scheduled to occurwithin the cluster may be associated with one or more routes. The set ofroutes for a regional cluster may include all of the routes associatedwith scheduled pickups within that cluster. Additionally, routes maymanually be assigned to a particular cluster. In some embodiments, theset of routes may include routes that pass through a regional cluster.For example, a route may be associated with each geographic area throughwhich it passes. If the regional cluster contains the geographic areapassed through by a route, then that route may be included in the set ofroutes. Each route may be associated with multiple regional clusters.

In some embodiments, each of processor devices 304(1-N) may be used toprocess one or more new pickups 306. In some embodiments, each newpickup 306 may be processed on a separate thread of a processor device.In accordance with at least some embodiments, a processor device mayprocess a new pickup by running one or more route optimizations 308 onthe identified set of routes. In some embodiments, a separate thread maybe used to run different optimization techniques for the same new pickup306. In some embodiments, the processor device may only be provided withthe identified set of routes and pickup locations associated with theregional cluster in order to increase computational efficiency, whileone or more other processor devices may only be provided with theidentified set of routes and pickup locations associated with one ormore other regional clusters, which processor devices may proceed withprocessing separately and/or in parallel.

FIG. 4 depicts an illustrative example of a system or architecture 400in which techniques for dynamically assigning a new order to pickuproutes may be implemented in accordance with at least some embodiments.In architecture 400, one or more consumers and/or users 402 may utilizeuser devices 404. In some examples, the user devices 404 may be incommunication with a service provider 406 via the network(s) 408, or viaother network connections.

The user devices 404 may be any type of computing device such as, butnot limited to, a mobile phone, a smart phone, a personal digitalassistant (PDA), a laptop computer, a desktop computer, a servercomputer, a thin-client device, a tablet PC, etc. Additionally, userdevices 404 may be any type of wearable technology device, such as anearpiece, glasses, etc. The user device 404 may include one or moreprocessors 410 capable of processing user input. The user device 404 mayalso include one or more input sensors 412 for receiving user input. Asis known in the art, there are a variety of input sensors 412 capable ofdetecting user input, such as accelerometers, cameras, microphones, etc.The user input obtained by the input sensors may be from a variety ofdata input types, including, but not limited to, audio data, visualdata, or biometric data. Embodiments of the application on the userdevice 404 may be stored and executed from its memory 414.

In some examples, the network(s) 408 may include any one or acombination of many different types of networks, such as cable networks,the Internet, wireless networks, cellular networks, and other privateand/or public networks. While the illustrated example represents theusers 402 accessing the browser application 416 over the network(s) 408,the described techniques may equally apply in instances where the users402 interact with a service provider 406 via the user device 404 over alandline phone, via a kiosk, or in any other manner. It is also notedthat the described techniques may apply in other client/serverarrangements (e.g., set-top boxes, etc.), as well as innon-client/server arrangements (e.g., locally stored applications, peerto-peer systems, etc.).

As described briefly above, the browser application 416 may allow theusers 402 to interact with a service provider 406, such as to store,access, and/or manage data, develop and/or deploy computer applications,and/or interact with web content. The one or more service providers 406,perhaps arranged in a cluster of servers or as a server farm, may beconfigured to host a website (or combination of websites) viewable viathe user device 404 or a web browser accessible by a user device 404 viathe browser application 416. Although depicted in memory of the userdevice 404 in this example, in some embodiments the browser application416 may be hosted at a server. For example, the user device 404 may be athin client device capable of accessing a browser application 416remotely. The browser application 416 may be capable of handlingrequests from many users 402 and serving, in response, various userinterfaces that can be rendered at the user device 404 such as, but notlimited to, a web site. The browser application 416 may be any type ofapplication or interface that supports user interaction with a website,including those with user interaction, such as social networking sites,electronic retailers, informational sites, blog sites, search enginesites, news and entertainment sites, and so forth. As discussed above,the described techniques can similarly be implemented outside of thebrowser application 416, such as with other applications running on theuser device 404.

The service provider 406 may be implemented by any type of computingdevice such as, but not limited to, a mobile phone, a smart phone, apersonal digital assistant (PDA), a laptop computer, a desktop computer,a server computer, a thin-client device, a tablet PC, etc. Additionally,it should be noted that in some embodiments, the service provider 406may be executed by one more virtual machines implemented in a hostedcomputing environment. The hosted computing environment may include oneor more rapidly provisioned and released computing resources, whichcomputing resources may include computing, networking, and/or storagedevices. A hosted computing environment may also be referred to as acloud-computing environment.

In one illustrative configuration, the service provider 406 may includeat least one memory 418 and one or more processing units (orprocessor(s)) 420. The processor(s) 420 may be implemented asappropriate in hardware, computer-executable instructions, firmware orcombinations thereof. Computer-executable instruction or firmwareimplementations of the processor(s) 420 may include computer-executableor machine executable instructions written in any suitable programminglanguage to perform the various functions described.

The memory 418 may store program instructions that are loadable andexecutable on the processor(s) 420, as well as data generated during theexecution of these programs. Depending on the configuration and type ofservice provider 406, the memory 418 may be volatile (such as randomaccess memory (RAM)) and/or non-volatile (such as read-only memory(ROM), flash memory, etc.). The service provider 406 may also includeadditional storage 422, such as either removable storage ornon-removable storage including, but not limited to, magnetic storage,optical disks, and/or tape storage. The disk drives and their associatedcomputer-readable media may provide non-volatile storage ofcomputer-readable instructions, data structures, program modules, andother data for the computing devices. In some implementations, thememory 418 may include multiple different types of memory, such asstatic random access memory (SRAM), dynamic random access memory (DRAM)or ROM. Turning to the contents of the memory 418 in more detail, thememory 418 may include an operating system 424 and one or moreapplication programs or services for implementing the features disclosedherein including at least a module for identifying one or more optimizedroutes (planning module 426) and/or a module for clustering item pickuplocations or vendors based on one or more attributes (clustering module428). The memory 418 may also include fulfillment data 430, whichprovides information related to routes, scheduled pickups, and iteminventory. In some embodiments, the fulfillment data 430 may be storedin a database.

The memory 418 and the additional storage 422, both removable andnon-removable, are examples of computer-readable storage media. Forexample, computer-readable storage media may include volatile ornon-volatile, removable or non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules or other data. As usedherein, modules may refer to programming modules executed by computingsystems (e.g., processors) that are part of the user device 404 or theservice provider 406. The service provider 406 may also containcommunications connection(s) 432 that allow the service provider 406 tocommunicate with a stored database, another computing device or server,user terminals, and/or other devices on the network(s) 408. The serviceprovider 406 may also include input/output (I/O) device(s) and/or ports434, such as for enabling connection with a keyboard, a mouse, a pen, avoice input device, a touch input device, a display, speakers, aprinter, etc.

Turning to the contents of the memory 418 in more detail, the memory 418may include an operating system 424, a database containing fulfillmentdata 430 and the one or more application programs or services forimplementing the features disclosed herein, including a planning module426 and/or a clustering module 428.

In some embodiments, the planning module 426 may be configured toreceive pickup scheduling data, as well as information related tocurrent routes, and identify a potentially optimum route. For example,in some embodiments, the planning module 426 may revise a number ofavailable routes to include a newly scheduled pickup. The planningmodule 426 may then determine which of the newly revised routes has, forexample, the least increase in overall cost, the least increase intravel distance, or the least increase in required resources.Alternatively, the planning module 426 may periodically create acompletely new set of routes based on current pickup information. Forexample, the planning module 426 may construct one or more sets ofroutes that encompass each of the pickups and from those sets, selectthe set of routes that is, for example, least costly, least traveldistance or least required resources. In this example, the addition of anew pickup may cause changes to several existing routes. It should beappreciated that a number of routing techniques are known in the art andany method of creating a pickup route, or set of pickup routes, shouldbe treated as an equivalent.

In some embodiments, the clustering module 428 may be configured toreceive pickup location data and identify various regions associatedwith high density pickup areas. Pickup location data may be related tohistorical pickup data, manually entered pickup data, purchase orderdata, and/or forecast data. Based on this pickup location data, theclustering module 428 may create multiple regional clusters within ageographic location, each of which may be processed by one or moreseparate processing devices. One role of the clustering module 428 maybe to identify appropriate regional clusters within a larger geographicarea. Although the clustering module 428 may utilize one or moreclustering techniques to do this (described above with respect to FIG.2), it may also assign regional clusters based on logical boundaries(e.g., geographic boundaries and/or man-made boundaries). For example,the clustering module 428 may identify one or more zip codes thatinclude a high density pickup area, and may assign the one or more zipcodes as a regional cluster. By separating a larger geographic area intoseparate regional clusters, the service provider may be able to utilizethe planning module 426 with respect to only those pickup routesrelevant to the regional cluster instead of all pickup routes. In thisway, the described technique provides a distributed solution to anNP-hard problem. In utilizing the described technique, computationalefficiency of route planning may be increased exponentially.

The fulfillment data 430 may be predetermined or it may be dynamicallygenerated. In some embodiments, fulfillment data 430 may contain routeinformation related to a number of pickup locations. For example, anumber of pickups may already each be assigned to a pickup route. Inthis example, the fulfillment data 430 may store the route information,the pickup information, and information about a relationship between thetwo. Fulfillment data 430 may also include purchase history or itemtrend data that may be used to forecast item demand.

FIG. 5 depicts an illustrative example of an inbound vehicle routingplatform 500 that dynamically adds a new order to pickup routes. In atleast some embodiments, the planning platform 500 that handles aparticular vendor's pickups is specific to that vendor's pickup locationor the regional cluster that includes that vendor's pickup location. Inparticular, the planning platform 500 that processes a particularvendor's pickups is one that processes each pickup in the same regionalcluster. To do this, each potential route for a regional cluster must beidentified for potential analysis. This may be done using routediscovery 502 techniques.

In at least some embodiments, route discovery 502 may involve analyzingexisting routes and vendors, such as those used in the past (extractedfrom historical data 504) and those entered through manual modeling 506(routes or vendors entered into the system and associated with theregional cluster by a user). Additionally, purchase order data 508 maybe used to determine what items are currently on order or will becomeavailable for pickup in the future. In some embodiments, forecastingdata 510 may be used to determine demand trends or inventory depletionrates that may be used to identify items that might need to be orderedsoon. Vendors associated with those items may then be identified. Onceeach vendor (potential and real) is identified in this fashion, routesassociated with that vendor may be discovered.

In at least some embodiments, discovered routes may be provided to oneor more processor devices 512. In accordance with at least someembodiments, collected route information may also be provided to afulfillment center dock calendar 514 that tracks shipment receivingdates at a fulfillment center. In some embodiments, routes may beconfigured according to configuration data 516. For example, routes maybe configured to avoid main roads and/or high traffic areas. In someembodiments, routes may be configured to reduce the number of left-handturns made by the delivery vehicle. In at least some embodiments,configuration data 516 may include settings that are specific to a typeof delivery vehicle associated with the delivery route. For example, ifa route includes a semi-truck as the delivery vehicle, the route may beconfigured according to the configuration data 516 related to the typesof roads and highways that semi-trucks may utilize (e.g., to avoidnarrow roads, certain tunnels or overpasses, etc.). In some embodiments,configuration data 516 that is relevant to a particular route may beprovided to planning module 518.

In at least some embodiments, a planning module 518 may use one or moreoptimization techniques, as well as any relevant configuration data 516,to determine an appropriate route to which a new pickup is to be added.In accordance with at least some embodiments, planning module 518 is anexample planning module 426 as depicted in FIG. 4. In at least someembodiments, multiple optimization techniques may be run and the resultsof each technique may be analyzed to select an appropriate route. In atleast some embodiments, multiple optimization techniques may be run inparallel, such as by separate processing devices or on separate threadsof a single processing device. Typically, the described optimizationtechniques are computationally expensive, however, because the datasetbeing optimized is limited to only those routes relevant to a particularregional cluster, several such optimization techniques may be used toidentify an optimal route. For example, some potential optimizationtechniques may include a greedy algorithm (e.g., a greedy bin packingalgorithm), a first fit decreasing algorithm, a multi knapsackalgorithm, a two opt algorithm, an exhaustive search algorithm, or alocal search algorithm. Additionally, one skilled in the art wouldrecognize that there are several equivalent optimization algorithms thatmay be used. In at least some embodiments the optimization techniquesmay take into account the weight and/or volume of the order. Forexample, in addition to identifying the most cost or operationallyeffective route for a particular delivery, the service provider mustensure that the delivery vehicle has the capacity to carry the order. Todo this, an optimization technique may determine whether various orderpickups must be moved to one or more different routes.

Once optimal route information is identified, a route plan 520 may beconstructed by the planning module 518. In some embodiments a planexecution workflow 522 is also created to include directives for makingthe one or more route changes included in the route plan. In at leastsome embodiments, the fulfillment center dock calendar 514 may beupdated to include data from the plan execution workflow 522. The planexecution workflow 522 may contain several sets of workflow directivesthat are configured to result in the pickup order being received at thefulfillment center. For example, one set of directives may be focused onassessing timelines for the pickup order. These directives may direct aprocessing device to determine whether other orders can be delayed toaccommodate a new pickup order. For example, prior to assigning a pickuporder to a particular route, the service provider may need to determinewhether that would cause the delivery vehicle to arrive at thefulfillment center later than is acceptable for the other pickup ordersserviced by the route. In another example, upon receiving an indicationthat a new pickup will be available after a given date, the serviceprovider may determine whether another route can be delayed until thatdate. In addition to determining whether orders can be delayed, aprocessing device may also be directed to determine whether orders maybe sped up. For example, upon determining that a new pickup order isneeded immediately (or within the near future), the service provider maydetermine whether other pickup orders may be picked up early in order tofill a delivery vehicle.

In some embodiments, a plan execution workflow 522 may directcommunications with various entities. For example, the plan executionworkflow 522 may communicate with a fulfillment center by directing aprocessing device to add shipment details to the fulfillment center'sdock calendar 514. In another example, the plan execution workflow 522may communicate with a carrier by directing a processing device tocreate carrier-specific plans and send those plans to the associatedcarrier. In another example, the plan execution workflow 522 maycommunicate with a vendor by directing a processing device to providepickup scheduling data to the vendor.

The route plan 520 is then provided to at least one carrier 524. Acarrier 524 is any transport service or vehicle that makes deliveries orpicks up orders. A carrier 524 may be under the employ of the serviceprovider, or it may be controlled by a third party entity. In at leastsome embodiments, carriers 524 may be dispatched along regular (e.g., aperiodically reoccurring) routes. The carriers 524 may be asked todeviate from a regular route or a previously scheduled route in order tomake the new pickup at its associated vendor 526. In some embodiments,planning platform 500 may perform the described processes as a new orderis received and one or more routes may be updated dynamically. Forexample, a carrier may be en route to a fulfillment center 528 when anew order is received from a vendor 526. In this example, the serviceprovider may determine that the carrier 524 is optimally situated tomake the pickup at the vendor 526 and may update the route provided tothe carrier to reflect the new pickup.

FIG. 6 depicts an illustrative flow chart demonstrating an example routealteration technique in accordance with at least some embodiments. Theprocess 600 is illustrated as a logical flow diagram, each operation ofwhich represents a sequence of operations that can be implemented inhardware, computer instructions, or a combination thereof. In thecontext of computer instructions, the operations representcomputer-executable instructions stored on one or more computer-readablestorage media that, when executed by one or more processors, perform therecited operations. Generally, computer-executable instructions includeroutines, programs, objects, components, data structures, and the likethat perform particular functions or implement particular data types.The order in which the operations are described is not intended to beconstrued as a limitation, and any number of the described operationscan be omitted or combined in any order and/or in parallel to implementthis process and any other processes described herein.

Some or all of the process 600 (or any other processes described herein,or variations and/or combinations thereof) may be performed under thecontrol of one or more computer systems configured with executableinstructions and may be implemented as code (e.g., executableinstructions, one or more computer programs or one or moreapplications). In accordance with at least one embodiment, the process600 of FIG. 6 may be performed by at least the one or more serviceproviders 406 shown in FIG. 4. The code may be stored on acomputer-readable storage medium, for example, in the form of a computerprogram including a plurality of instructions executable by one or moreprocessors. The computer-readable storage medium may be non-transitory.

In accordance with at least some embodiments, process 600 may begin at602 when a new pickup order is received. In some embodiments, theservice provider may identify a product demand for one or more itemsincluded with the pickup order at 604. An item's demand may include adate by which the item is predicted to be needed. For example, theservice provider may calculate a date upon which the item's inventory isscheduled to be depleted based on a rate at which the item is beingpurchased by consumers and a current inventory. The service provider maydetermine, based on the item's demand, whether the pickup can be delayedat 606. If the item is needed immediately (meaning that no delay isacceptable), then the service provider may need to schedule a separate,dedicated pickup for the item at 608.

The service provider may cluster vendors at 610. In some embodiments,the service provider may cluster vendors by one or more attributes ofthe vendor pickup location. For example, the service provider maycluster vendors by accessibility (what vehicle types are able to accessand service a particular vendor), whether a loading dock is present, bygeographic region, or by any other suitable attribute of a vendor pickuplocation. In some embodiments, the service provider may cluster vendorsby an attribute of the pickup order. For example, orders that requirerefrigeration may be placed into a separate cluster. As a secondexample, orders that require loading via a forklift may be placed into aseparate cluster. The service provider may also create sub-clusters, inwhich vendors are clustered by two or more attributes. For example,vendors may be clustered based on an order's need for refrigeration aswell as the vendor pickup location's geographic region. In someembodiments, process step 610 may be performed in real-time (as newinformation is received) or it may be performed periodically.

Once the vendors have been clustered by the service provider, theservice provider may identify one or more routes associated with thevendor pickups of a particular cluster at 612. For example, each pickuproute that services a vendor within the regional cluster may beidentified and added to a set of routes. In some embodiments, each ofthe routes may be associated with a date and time upon which a deliveryvehicle associated with the route is expected to arrive at a fulfillmentcenter. The service provider may also store an indication of thevehicle's expected location at specific times. In some embodiments, theservice provider may monitor delivery vehicles in real-time, via globalpositioning service (GPS). Once a set of routes has been identified bythe service provider, the service provider may filter routes accordingto whether the route's arrival time is aligned with a demand date foreach item within the order. By filtering the routes in this manner, theservice provider may determine whether there is an available route at614. If none of the routes would result in the order arriving by thetime that it is needed, the service provider may determine whether otherpickups can be made earlier at 616. For example, the service providermay analyze pickup time windows provided by vendors to determine whetherorders may be picked up earlier than they are currently scheduled. Ifthis is not the case, then the service provider may schedule a separate,dedicated shipment for the order, as depicted at 608. If, however, someitems may be picked up earlier than they are scheduled for, the serviceprovider may calculate new routes to include the earlier pickup times aswell as the new pickup order.

Once the service provider is able to determine a subset of routes thatmay be used to get the order to a fulfillment center by the time that itis needed, the service provider may perform one or more optimizationtechniques on that subset of routes to identify an optimal route at 618,which may be altered to include the pickup order at 620. In someembodiments, the service provider may perform several route optimizationtechniques in parallel. In some embodiments, the service provider mayshift orders between routes in the set of routes based on weight orvolume requirements. For example, the service provider may utilize oneor more optimization techniques to construct a whole new set of routesfor all of the pickups in a regional cluster using volume and weightconstraints of the associated delivery vehicles. In some embodiments,the service provider may merely alter a single route to include theorder. Once a set of routes has been created to accommodate the newpickup order, each of the revised routes may be provided to itscorresponding carrier at 622, for example, on a periodic or scheduledbasis. In some embodiments, the route may not be provided to a carrieruntil it is on the route. For example, the service provider may onlyprovide an indication of the carrier's next destination at any giventime.

In some embodiments, a service provider may reconstruct a number ofroutes to be associated with a particular cluster. In some cases, it maybe beneficial to adjust the number of carriers and/or routes servicingthe regional cluster. Prior to constructing a set of routes for acluster, the service provider may utilize one or more machine learningtechniques to estimate a number of routes that may be appropriate forthe regional cluster. The service provider may then estimate the typesand/or the number of carriers desired to service the cluster andsubsequently may construct the set of routes. This allows a serviceprovider to procure capacity in advance and may result in reducedtransit time and/or delivery costs.

In determining a number of routes/carriers appropriate for a cluster,the service provider may identify one or more constraints such ascapacity (e.g., weight and/or volume), accessibility (the types ofvehicles able to access each pickup location), traffic speed (the typesof vehicles most appropriate for traffic conditions), or any othersuitable constraint factor. For example, the service provider mayestimate a number of carriers desired for a cluster based on a totalweight and/or volume of the pickups in that cluster. In this example,the total estimated weight and volume constraints for a particularcluster for a period of time may be used to determine the number andtypes of carriers desired during that period of time. Once carriers havebeen identified, a set of routes may be constructed for the carriers.

In accordance with at least some embodiments, the service provider mayestimate constraint data for a particular cluster using one or moremachine learning or pattern recognition algorithms. For example, aservice provider may review historical routing data, historical purchaseorder data, current purchase order data, and/or purchase order forecastdata in order to determine present and/or future constraint requirementsfor a cluster. In this example, the service provider may utilize one ormore machine learning algorithms (e.g., a time series modellingalgorithm, a regression algorithm, or the like) to forecast the typesand number of carriers desired to service the cluster.

FIG. 7 depicts an illustrative flow diagram depicting an example routeupdating process in accordance with at least some embodiments. Theprocess 700 is illustrated as a logical flow diagram, each operation ofwhich represents a sequence of operations that can be implemented inhardware, computer instructions, or a combination thereof. In thecontext of computer instructions, the operations representcomputer-executable instructions stored on one or more computer-readablestorage media that, when executed by one or more processors, perform therecited operations. Generally, computer-executable instructions includeroutines, programs, objects, components, data structures, and the likethat perform particular functions or implement particular data types.The order in which the operations are described is not intended to beconstrued as a limitation, and any number of the described operationscan be omitted or combined in any order and/or in parallel to implementthis process and any other processes described herein.

Some or all of the process 700 (or any other processes described herein,or variations and/or combinations thereof) may be performed under thecontrol of one or more computer systems configured with executableinstructions and may be implemented as code (e.g., executableinstructions, one or more computer programs or one or moreapplications). In accordance with at least one embodiment, the process700 of FIG. 7 may be performed by at least the one or more serviceproviders 406 shown in FIG. 4. The code may be stored on acomputer-readable storage medium, for example, in the form of a computerprogram including a plurality of instructions executable by one or moreprocessors. The computer-readable storage medium may be non-transitory.

In accordance with at least some embodiments, process 700 may begin at702 when information is received by the service provider that a newpickup order is available. The information may include a list of itemsthat are included in the new pickup order, a date upon which the pickuporder is available to be picked up, information related to the locationof the pickup order, as well as any other suitable information relatedto the pickup order. In at least some embodiments, the service providermay determine a date by which at least one of the items of the order isneeded at 704. For example, the service provider may forecast that aninventory of at least one of the item will be completely depleted withinfive days. In this example, the service provider may set a need-by dateof the current date plus five days.

In accordance with at least some embodiments, the service provider mayseparate the vendors from which it receives pickup orders into a numberof groups or clusters at 706. In some embodiments, the groups may bebased on region or geographic location. Once the vendors have each beenassigned to a group, any routes associated with the vendor may then beassociated with the group at 708. For example, if Vendor A is assignedto a region X, and route 1 and route 2 are both scheduled to result in acollection of separate orders from Vendor A, then both route 1 and route2 may be associated with region X. The service provider may thendetermine a particular route to alter in order to include the new pickuporder at 710. In some embodiments, only those routes that are associatedwith a region may be processed via an optimization technique. Processingonly these routes by region in this fashion may make runningoptimization techniques more computationally feasible than running aroute optimization on a larger set of routes and/or regions.

In accordance with at least some embodiments, one or more routes in theset of routes may be updated to include the optimization results at 712.For example, the service provider may determine that an assignment ofthe pickup order to a route 1 would result in a greater cost savings (ora lower cost increase). In this example, the service provider may updatethe set of routes by replacing the current route 1 with an altered route1 that includes the new pickup order. In some embodiments, the serviceprovider may construct a new set of routes to replace the current set ofroutes. For example, the service provider may determine that another setof routes is less costly than, or results in an increase of efficiencyover, the current set of routes. In this example, the set of routes maybe updated such that the current set of routes is replaced with the newset of routes. Once the set of routes has been updated, the updatedroute information may be provided to one or more carriers responsiblefor making pickups of the route at 714. In some embodiments the updatedroute information may be provided in the form of route guidance or mapdata.

FIG. 8 illustrates aspects of an example environment 800 forimplementing aspects in accordance with various embodiments. As will beappreciated, although a Web-based environment is used for purposes ofexplanation, different environments may be used, as appropriate, toimplement various embodiments. The environment includes an electronicclient device 802, which can include any appropriate device operable tosend and receive requests, messages or information over an appropriatenetwork 804 and convey information back to a user of the device.Examples of such client devices include personal computers, cell phones,handheld messaging devices, laptop computers, set-top boxes, personaldata assistants, electronic book readers and the like. The network caninclude any appropriate network, including an intranet, the Internet, acellular network, a local area network or any other such network orcombination thereof. Components used for such a system can depend atleast in part upon the type of network and/or environment selected.Protocols and components for communicating via such a network are wellknown and will not be discussed herein in detail. Communication over thenetwork can be enabled by wired or wireless connections and combinationsthereof. In this example, the network includes the Internet, as theenvironment includes a Web server 806 for receiving requests and servingcontent in response thereto, although for other networks an alternativedevice serving a similar purpose could be used as would be apparent toone of ordinary skill in the art.

The illustrative environment includes at least one application server808 and a data store 810. It should be understood that there can beseveral application servers, layers, or other elements, processes orcomponents, which may be chained or otherwise configured, which caninteract to perform tasks such as obtaining data from an appropriatedata store. As used herein the term “data store” refers to any device orcombination of devices capable of storing, accessing and retrievingdata, which may include any combination and number of data servers,databases, data storage devices and data storage media, in any standard,distributed or clustered environment. The application server can includeany appropriate hardware and software for integrating with the datastore as needed to execute aspects of one or more applications for theclient device, handling a majority of the data access and business logicfor an application. The application server provides access controlservices in cooperation with the data store and is able to generatecontent such as text, graphics, audio and/or video to be transferred tothe user, which may be served to the user by the Web server in the formof HyperText Markup Language (“HTML”), Extensible Markup Language(“XML”) or another appropriate structured language in this example. Thehandling of all requests and responses, as well as the delivery ofcontent between the client device 802 and the application server 808,can be handled by the Web server. It should be understood that the Weband application servers are not required and are merely examplecomponents, as structured code discussed herein can be executed on anyappropriate device or host machine as discussed elsewhere herein.

The data store 810 can include several separate data tables, databasesor other data storage mechanisms and media for storing data relating toa particular aspect. For example, the data store illustrated includesmechanisms for storing production data 812 and user information 816,which can be used to serve content for the production side. The datastore also is shown to include a mechanism for storing log data 814,which can be used for reporting, analysis or other such purposes. Itshould be understood that there can be many other aspects that may needto be stored in the data store, such as for page image information andto access right information, which can be stored in any of the abovelisted mechanisms as appropriate or in additional mechanisms in the datastore 810. The data store 810 is operable, through logic associatedtherewith, to receive instructions from the application server 808 andobtain, update or otherwise process data in response thereto. In oneexample, a user might submit a search request for a certain type ofitem. In this case, the data store might access the user information toverify the identity of the user and can access the catalog detailinformation to obtain information about items of that type. Theinformation then can be returned to the user, such as in a resultslisting on a Web page that the user is able to view via a browser on theuser device 802. Information for a particular item of interest can beviewed in a dedicated page or window of the browser.

Each server typically will include an operating system that providesexecutable program instructions for the general administration andoperation of that server and typically will include a computer-readablestorage medium (e.g., a hard disk, random access memory, read onlymemory, etc.) storing instructions that, when executed by a processor ofthe server, allow the server to perform its intended functions. Suitableimplementations for the operating system and general functionality ofthe servers are known or commercially available and are readilyimplemented by persons having ordinary skill in the art, particularly inlight of the disclosure herein.

The environment in one embodiment is a distributed computing environmentutilizing several computer systems and components that areinterconnected via communication links, using one or more computernetworks or direct connections. However, it will be appreciated by thoseof ordinary skill in the art that such a system could operate equallywell in a system having fewer or a greater number of components than areillustrated in FIG. 8. Thus, the depiction of the system 800 in FIG. 8should be taken as being illustrative in nature and not limiting to thescope of the disclosure.

The various embodiments further can be implemented in a wide variety ofoperating environments, which in some cases can include one or more usercomputers, computing devices or processing devices which can be used tooperate any of a number of applications. User or client devices caninclude any of a number of general purpose personal computers, such asdesktop or laptop computers running a standard operating system, as wellas cellular, wireless and handheld devices running mobile software andcapable of supporting a number of networking and messaging protocols.Such a system also can include a number of workstations running any of avariety of commercially-available operating systems and other knownapplications for purposes such as development and database management.These devices also can include other electronic devices, such as dummyterminals, thin-clients, gaming systems and other devices capable ofcommunicating via a network.

Most embodiments utilize at least one network that would be familiar tothose skilled in the art for supporting communications using any of avariety of commercially-available protocols, such as TransmissionControl Protocol/Internet Protocol (“TCP/IP”), Open SystemInterconnection (“OSI”), File Transfer Protocol (“FTP”), Universal Plugand Play (“UpnP”), Network File System (“NFS”), Common Internet FileSystem (“CIFS”) and AppleTalk. The network can be, for example, a localarea network, a wide-area network, a virtual private network, theInternet, an intranet, an extranet, a public switched telephone network,an infrared network, a wireless network and any combination thereof.

In embodiments utilizing a Web server, the Web server can run any of avariety of server or mid-tier applications, including Hypertext TransferProtocol (“HTTP”) servers, FTP servers, Common Gateway Interface (“CGI”)servers, data servers, Java servers and business application servers.The server(s) also may be capable of executing programs or scripts inresponse requests from user devices, such as by executing one or moreWeb applications that may be implemented as one or more scripts orprograms written in any programming language, such as Java®, C, C# orC++, or any scripting language, such as Perl, Python or TCL, as well ascombinations thereof. The server(s) may also include database servers,including without limitation those commercially available from Oracle®,Microsoft®, Sybase® and IBM®.

The environment can include a variety of data stores and other memoryand storage media as discussed above. These can reside in a variety oflocations, such as on a storage medium local to (and/or resident in) oneor more of the computers or remote from any or all of the computersacross the network. In a particular set of embodiments, the informationmay reside in a storage-area network (“SAN”) familiar to those skilledin the art. Similarly, any necessary files for performing the functionsattributed to the computers, servers or other network devices may bestored locally and/or remotely, as appropriate. Where a system includescomputerized devices, each such device can include hardware elementsthat may be electrically coupled via a bus, the elements including, forexample, at least one central processing unit (“CPU”), at least oneinput device (e.g., a mouse, keyboard, controller, touch screen orkeypad) and at least one output device (e.g., a display device, printeror speaker). Such a system may also include one or more storage devices,such as disk drives, optical storage devices and solid-state storagedevices such as random access memory (“RAM”) or read-only memory(“ROM”), as well as removable media devices, memory cards, flash cards,etc.

Such devices also can include a computer-readable storage media reader,a communications device (e.g., a modem, a network card (wireless orwired), an infrared communication device, etc.) and working memory asdescribed above. The computer-readable storage media reader can beconnected with, or configured to receive, a computer-readable storagemedium, representing remote, local, fixed and/or removable storagedevices as well as storage media for temporarily and/or more permanentlycontaining, storing, transmitting and retrieving computer-readableinformation. The system and various devices also typically will includea number of software applications, modules, services or other elementslocated within at least one working memory device, including anoperating system and application programs, such as a client applicationor Web browser. It should be appreciated that alternate embodiments mayhave numerous variations from that described above. For example,customized hardware might also be used and/or particular elements mightbe implemented in hardware, software (including portable software, suchas applets) or both. Further, connection to other computing devices suchas network input/output devices may be employed.

Storage media and computer readable media for containing code, orportions of code, can include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information such as computer readable instructions, data structures,program modules or other data, including RAM, ROM, Electrically ErasableProgrammable Read-Only Memory (“EEPROM”), flash memory or other memorytechnology, Compact Disc Read-Only Memory (“CD-ROM”), digital versatiledisk (DVD) or other optical storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices or any othermedium which can be used to store the desired information and which canbe accessed by the a system device. Based on the disclosure andteachings provided herein, a person of ordinary skill in the art willappreciate other ways and/or methods to implement the variousembodiments.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the disclosure asset forth in the claims.

Other variations are within the spirit of the present disclosure. Thus,while the disclosed techniques are susceptible to various modificationsand alternative constructions, certain illustrated embodiments thereofare shown in the drawings and have been described above in detail. Itshould be understood, however, that there is no intention to limit thedisclosure to the specific form or forms disclosed, but on the contrary,the intention is to cover all modifications, alternative constructionsand equivalents falling within the spirit and scope of the disclosure,as defined in the appended claims.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosed embodiments (especially in thecontext of the following claims) are to be construed to cover both thesingular and the plural, unless otherwise indicated herein or clearlycontradicted by context. The terms “comprising,” “having,” “including,”and “containing” are to be construed as open-ended terms (i.e., meaning“including, but not limited to,”) unless otherwise noted. The term“connected” is to be construed as partly or wholly contained within,attached to, or joined together, even if there is something intervening.Recitation of ranges of values herein are merely intended to serve as ashorthand method of referring individually to each separate valuefalling within the range, unless otherwise indicated herein and eachseparate value is incorporated into the specification as if it wereindividually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context. The use of any and allexamples, or exemplary language (e.g., “such as”) provided herein, isintended merely to better illuminate embodiments of the disclosure anddoes not pose a limitation on the scope of the disclosure unlessotherwise claimed. No language in the specification should be construedas indicating any non-claimed element as essential to the practice ofthe disclosure.

Disjunctive language such as the phrase “at least one of X, Y, or Z,”unless specifically stated otherwise, is intended to be understoodwithin the context as used in general to present that an item, term,etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y,and/or Z). Thus, such disjunctive language is not generally intended to,and should not, imply that certain embodiments require at least one ofX, at least one of Y, or at least one of Z to each be present.

Preferred embodiments of this disclosure are described herein, includingthe best mode known to the inventors for carrying out the disclosure.Variations of those preferred embodiments may become apparent to thoseof ordinary skill in the art upon reading the foregoing description. Theinventors expect skilled artisans to employ such variations asappropriate and the inventors intend for the disclosure to be practicedotherwise than as specifically described herein. Accordingly, thisdisclosure includes all modifications and equivalents of the subjectmatter recited in the claims appended hereto as permitted by applicablelaw. Moreover, any combination of the above-described elements in allpossible variations thereof is encompassed by the disclosure unlessotherwise indicated herein or otherwise clearly contradicted by context.

All references, including publications, patent applications and patents,cited herein are hereby incorporated by reference to the same extent asif each reference were individually and specifically indicated to beincorporated by reference and were set forth in its entirety herein.

What is claimed is:
 1. A computer-implemented method, comprising:receiving order data including information related to an item, anavailability date for the item, an identification of a fulfillmentcenter to receive the item, and location information associated with apickup of the item; determining, based at least in part on an inventorydemand for the item, a deliver-by date for the item; identifying, basedat least in part on the location information, a regional cluster to beassociated with the item, wherein the regional cluster is identifiedusing one or more clustering techniques to determine a centroid from aplurality of centroids to which the pickup of the item belongs, whereinthe regional cluster surrounds the determined centroid; identifying aset of delivery routes related to the availability date, the deliver-bydate, and the regional cluster, each of the delivery routes in the setof delivery routes being a previously scheduled delivery route thatincludes delivery to the fulfillment center; determining, based at leastin part on at least one efficiency metric associated with one or more ofthe set of delivery routes, a delivery route from the set of deliveryroutes to include the item; and causing the set of delivery routes to beupdated such that the determined delivery route from the set of deliveryroutes includes a delivery pickup related to the received order data. 2.The computer-implemented method of claim 1, further comprising providingthe determined delivery route to a carrier, the carrier being instructedto make the delivery pickup.
 3. The computer-implemented method of claim2, wherein the carrier is a third-party entity.
 4. Thecomputer-implemented method of claim 1, wherein the delivery route fromthe set of delivery routes is determined based at least in part on aweight or a volume of the item.
 5. The computer-implemented method ofclaim 1, wherein locations associated with each of the centroids in theplurality of centroids are recalculated on a periodic basis.
 6. Thecomputer-implemented method of claim 1, wherein each of the deliveryroutes in the set of delivery routes originates somewhere other than thefulfillment center.
 7. A system comprising: a processor; and a memoryincluding instructions that, when executed with the processor, cause thesystem to at least: identify one or more pickup regions, each of the oneor more pickup regions associated with a respective plurality ofvendors, the pickup regions being identified using one or moreclustering techniques to identify a plurality of centroids to which theplurality of vendors belongs, the pickup regions each surrounding anidentified centroid; receive, from a vendor of the plurality of vendors,an indication that an item is available for pickup; identify, based atleast in part on the received indication, a pickup region of the one ormore pickup regions associated with the vendor of the plurality ofvendors; identify a set of delivery routes associated with the pickupregion; determine, based at least in part on an efficiency value, adelivery route of the set of delivery routes to include the itemavailable for pickup; and update the delivery route to include the itemavailable for pickup.
 8. The system of claim 7, wherein at least oneoptimization technique is used to determine the delivery route of theset of delivery routes to include the item available for pickup.
 9. Thesystem of claim 8, wherein multiple optimization techniques are used todetermine the delivery route of the set of delivery routes to includethe item available for pickup, the delivery route of the set of deliveryroutes determined based at least in part on a result of at least one ofthe multiple optimization techniques.
 10. The system of claim 9, whereinthe multiple optimization techniques are performed in parallel.
 11. Thesystem of claim 7, wherein the instructions further cause the system toat least: determine a demand date for the item available for pickup; andfilter the set of delivery routes associated with the pickup region suchthat the set of delivery routes includes routes that meet the demanddate.
 12. The system of claim 11, wherein the demand date is determinedbased at least in part on a depletion rate for the item and a currentinventory amount of the item.
 13. The system of claim 11, wherein thedemand date is determined based at least in part on outstanding ordersfor the item.
 14. The system of claim 7, wherein the set of deliveryroutes is identified based at least in part on delivery routes of theset of delivery routes being associated with at least one second itemavailable for pickup within the pickup region.
 15. The system of claim7, wherein each of the one or more pickup regions associated with therespective plurality of vendors is defined by one or more geographicboundaries.
 16. A non-transitory computer readable medium storingspecific computer-executable instructions that, when executed by one ormore processors, cause a computer system to perform operationscomprising: identifying, based at least in part on collective pickupdata, a plurality of origination regions each associated with arespective plurality of vendor locations, the origination regions beingidentified using one or more clustering techniques to identify aplurality of centroids to which the plurality of vendor locationsbelongs, the origination regions each surrounding an identifiedcentroid; receiving information related to a new item pickup, theinformation including an indication of an origination region of theplurality of origination regions; identifying, from a set of pickuproutes associated with the origination region of the plurality oforigination regions, a pickup route to include the new item pickup, thepickup route being identified based at least in part on a routeoptimization of the set of pickup routes; and providing, to a pickupdriver associated with the identified pickup route, the informationrelated to the new item pickup.
 17. The computer readable medium ofclaim 16, wherein the collective pickup data includes informationrelated to past pickup locations.
 18. The computer readable medium ofclaim 16, wherein the information related to the new item pickupincludes a first time at which the new item pickup becomes available,and wherein the set of pickup routes is identified based at least inpart on the first time.
 19. The computer readable medium of claim 16,wherein the set of pickup routes associated with the origination regionof the plurality of origination regions includes at least one route thatpasses through the origination region.
 20. The computer readable mediumof claim 16, wherein the information related to the new item pickupprovided to the pickup driver is a route map.
 21. The computer readablemedium of claim 16, wherein the pickup route is identified from the setof pickup routes associated with the origination region using a firstprocessor of the one or more processors, the first processor beingassociated with the origination region.
 22. The computer readable mediumof claim 16, wherein the pickup route is identified based at least inpart on a cost associated with the pickup route.